Performance Comparison of Standard Polysomnographic Parameters Used in the Diagnosis of Sleep Apnea

Year 2024, Volume: 19 Issue: 1, 257 - 263, 28.03.2024

Seda Arslan Tuncer Yakup Çiçek Taner Tuncer

https://doi.org/10.55525/tjst.1419740

Abstract

Obstructive sleep apnea (OSAS), which is one of the leading sleep disorders and can result in death if not diagnosed and treated early, is most often confused with snoring. OSAS disease, the prevalence of which varies between 0.9% and 1.9% in Turkey, is a serious health problem that occurs as a result of complete or partial obstruction of the respiratory tract during sleep, resulting in sleep disruption, poor quality sleep, paralysis and even death in sleep. Polysomnography signal recordings (PSG) obtained from sleep laboratories are used for the diagnosis of OSAS, which is related to factors such as the individual's age, gender, neck diameter, smoking-alcohol consumption, and the occurrence of other sleep disorders. Polysomnography is used in the diagnosis and treatment of sleep disorders such as snoring, sleep apnea, parasomnia (abnormal behaviors during sleep), narcolepsy (sleep attacks that develop during the day) and restless legs syndrome. It allows recording various parameters such as brain waves, eye movements, heart and chest activity measurement, respiratory activities, and the amount of oxygen in the blood with the help of electrodes placed in different parts of the patient's body during night sleep. In this article, the performance of PSG signal data for the diagnosis of sleep apnea was examined on the basis of both signal parameters and the method used. First, feature extraction was made from PSG signals, then the feature vector was classified with Artificial Neural Networks, Support Vector Machine (SVM), k-Nearest Neighbors (k-NN) and Logistic Regression (LR).

Keywords

Sleep, Sleep Apnea, PSG, Classification

Uyku Apnesinin Teşhisinde Kullanılan Standart Polisomnografik Parametrelerin Performans Karşılaştırılması

Abstract

Erken tanı konulmadığında ve tedavi edilmediği zaman ölümle sonuçlanabilen ve uyku hastalıklarının başından gelen Tıkayıcı uyku apnesi (OSAS) en çok horlama ile karıştırılmaktadır. Türkiye’de görülme yaygınlığı %0,9 ila %1,9 değişen OSAS hastalığı uyku süresi boyunca solunum yollarnın tamamen veya kısmen tıkanması sonucunda görülen uyku bölünmesi, kalitesiz uyku geçirme, felç olma ve hatta uykuda ölümün bile görülmesi gibi sonuçlar doğuran ciddi bir sağlık sorunudur. Bireyin yaşı, cinsiyeti boyun çapı, sigara-alkol tüketimi, diğer uyku rahatsızlıkların görülme durumu gibi etmenlerle ilişkili olan OSAS tanı için uyku laboratuvarlarından alınan Polisomnografi sinyal kayıtları (PSG) kullanılmaktadır. Polisomnografi horlama, uyku apnesi, parasomnia (uyku esnasında anormal davranışlar), narkolepsi (gün içinde gelişen uyku atakları), huzursuz bacak sendromu gibi uyku bozukluklarının tanı ve tedavisinde kullanılır. Gece uykusu boyunca hasta vücudunun farklı bölgelerine yerleştirilen elektrotlar yardımıyla beyin dalgaları, göz hareketleri, kalp ve göğüs aktivitesinin ölçülmesi, solunum etkinlikleri, kandaki oksijen miktarı gibi çeşitli parametrelerin kayıt altına alınmasını sağlar. Bu makalede, PSG sinyal verilerinin uyku apnesinin teşhisine yönelik başarımları hem sinyal parametreleri hem de kullanılan yöntem bazında incelendi. İlk olarak PSG sinyallerinden özellik çıkartımı yapıldı daha sonra özellik vektörü yapay sinir ağları, destek vektör makinesi (DVM), k-enyakın komşu (k-NN) ve lojistik regrasyon (LR) ile sınıflandırıldı.

Keywords

Uyku, Uyku Apnesi, PSG, Sınıflandırma

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